Optimal and heuristic policies for lot sizing with learning in setups

Abstract

Abstract We develop heuristic and optimal methods for determining lot sizes when setup cost reductions occur over time due to emphasis on continuous improvement, learning effects and incremental process changes. Our heuristic methods are intuitively appealing, easy to implement, require little information about setup costs, and have low computational burden. Computational studies show that choosing the appropriate heuristic yields nearly optimal solutions. Recommendations for choosing the appropriate heuristic are also provided. The optimal method developed in this paper is also useful to managers for evaluating investments in hardware and/or worker training for setup reduction. Concepts and methods are illustrated with numerical examples. Managerial implications of using our policies are discussed.

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